Usefulness of chemometrics and mass spectrometry-based electronic nose to classify Australian white wines by their varietal origin

A combination of mass spectrometry-based electronic nose (MS e_nose) and chemometrics was explored to classify two Australian white wines according to their varietal origin namely Riesling and unwooded Chardonnay. The MS e_nose data were analysed using principal components analysis (PCA), discriminant partial least squares (DPLS) and linear discriminant analysis (LDA) applied to principal components scores and validated using full cross validation (leave one out). DPLS gave the highest levels of correct classification for both varieties (>90%). LDA classified correctly 73% of unwooded Chardonnay and 82% of Riesling wines. Even though the conventional analysis provides fundamental information about the volatile compounds present in the wine, the MS e_nose method has a series of advantages over conventional analytical techniques due to simplicity of the sample-preparation and reduced time of analysis and might be considered as a more convenient choice for routine process control in an industrial environment. The work reported here is a feasibility study and requires further development with considerably more commercial samples of different varieties. Further studies are needed in order to improve the calibration specificity, accuracy and robustness, and to extend the discrimination to other wine varieties or blends.     

A 21st century technique for food control: Electronic noses

This work examines the main features of modern electronic noses (e-noses) and their most important applications in food control in this new century. The three components of an electronic nose (sample handling system, detection system, and data processing system) are described. Special attention is devoted to the promising mass spectrometry based e-noses, due to their advantages over the more classical gas sensors. Applications described include process monitoring, shelf-life investigation, freshness evaluation, authenticity assessment, as well as other general aspects of the utilization of electronic noses in food control. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in food control are also mentioned.     

Geographical Authentication of Gentiana Rigescens by High-Performance Liquid Chromatography and Infrared Spectroscopy

The chemical characteristics of Gentiana rigescens are extremely variable due to their geographical origins which should be determined to evaluate the quality of this species. Different with other herbs with official tissue for classification materials, the geographical characterization of raw herbal materials on the basis of nonmedicinal parts is rarely discussed. Chromatographic active components were used as references to characterize the chemical profiles of samples from various geographical origins. Based on spectra data matrix of different botanical parts, the chemometric methods of partial least square discrimination analysis and support vector machine discrimination analysis were used to develop mathematical models to classify samples from different geographical origins. In terms of six active components, we found that significant differences were present in the tissue of G. rigescens based on geographical origins. In addition, the region with higher content of gentiopicroside was selected to be the optimal cultivated location. Chemometric results indicated that leaves were the optimal material for geographical characterization of G. rigescens with 100% accuracy by support vector machine while the accuracies of roots, stems, and flowers were 90.91, 96.10, and 97.01%, respectively. Partial least square discrimination analysis showed that accuracy values for roots, stems, leaves, and flowers were 35.65, 67.53, 76.62, and 50.75%, respectively, which also indicated that leaves are the optimal material. In conclusion, northwest Yunnan Province with higher content of gentiopicroside was selected to be the optimal cultivation location. Furthermore, leaves should be used for the most accurate geographical authentication.     

Partial least‐squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with ¹H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright © 2015 John Wiley & Sons, Ltd.     

Nano‐Graphene Oxide Based Multichannel Sensor Arrays towards Sensing of Protein Mixtures

Optical array‐based sensors are attractive candidates for the detection of various bio‐analytes due to their convenient fabrication and measurements. For array‐based sensors, multichannel arrays are more advantageous and used frequently in many electronic sensors. But most reported optically array based sensors are constructed on a single channel array. This difficulty is mainly instigated from the overlap in optical responses. In this report we have used nano‐graphene oxide (nGO) and suitable fluorophores as sensor elements to construct a multichannel sensor array for the detection of protein analytes. By using the optimized multichannel array we are able to detect different proteins and mixtures of proteins with 100 % classification accuracy at sub‐nanomolar concentration. This modified method expedites the sensing analysis as well as minimizes the use of both analyte and sensor elements in array‐based protein sensing. We have also used this system for the single channel array‐based sensing to compare the sensitivity and the efficacy of these two systems for other applications. This work demonstrated an intrinsic trade‐off associated with these two methods which may be necessary to balance for array‐based analyte detections.     

Boosting partial least‐squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd.     

福尔马林固定石蜡包埋组织切片蛋白质组分析方法与应用研究进展

福尔马林固定石蜡包埋(Formalin-fixed and paraffin-embedding,FFPE)是最常见的临床组织保存技术,FFPE组织具有标准化的制备流程、易于存储、标本量大、包含较完整的临床回顾性信息等特点,而成为疾病生物标志物发掘的良好载体。近年来,基于临床FFPE组织的特点,发展了系列蛋白质组学研究方法,包括样本制备、消解、分离到蛋白质质谱鉴定等多个领域,总体呈现出微量、高灵敏度、高通量的技术特点,并已成功用于肿瘤精准医学等临床蛋白质组研究。该综述将对FFPE组织切片样本的蛋白质组学方法,以及其在肿瘤研究中的应用进行概述,从而为临床精准医学蛋白质组学的研究提供借鉴思路。     

PLS‐DA for compositional data with application to metabolomics

When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS‐DA)—the most widely used method in chemometrics for multivariate classification—can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters (metabolites) using a small data set resulting from targeted metabolomics, where just a subset of potential markers is selected. The second example—the approach of untargeted metabolomics—was used for the analysis detecting almost 500 metabolites. The significance of the metabolites is investigated by applying PLS‐DA, accommodated according to a compositional approach. The significance of important metabolites (markers of diseases) is more clearly visible with the compositional method in both examples. Also, cross‐validation methods lead to better results in case of using the compositional approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Characterization of Gasoline by 1H Nuclear Magnetic Resonance and Chemometrics

Automotive fuel adulteration is an old and significant problem. One common type of fuel adulteration is the addition of diesel to gasoline. Unsupervised models were developed through hierarchical cluster and principal component analysis models. Supervised models through partial least square discriminant analysis using ¹H nuclear magnetic resonance spectra as the input were used to classify samples as adulterated or unadulterated. Quantitative models were developed using partial least squares to determine the gasoline and diesel concentrations in the samples. This set contained samples composed of pure gasoline and anhydrous ethanol reproducing commercial gasoline and other samples treated with diesel. Hierarchical cluster and principal component analysis did not distinguish between adulterated and unadulterated samples except for the most adulterated materials. However, partial least square discriminant analysis classified 100% of the samples correctly. The partial least square algorithm provided excellent regression models for the gasoline and diesel content. The determination coefficient was 0.9920 for both models, whereas the root mean square error of cross-validation and root mean square error of prediction for the diesel model were 2.32 and 1.42%, respectively, and 2.40 and 1.38% for the gasoline model.     

基于串级质谱信息进行蛋白质数据库搜索的结果可靠性分析

SEQUEST与Mascot为目前蛋白组学分析研究中使用最为广泛的蛋白质库搜索工具.尝试将Mascot与SEQUEST搜索结果进行比较,进而采用不同多变量判别方法对二者的搜索结果进行判别分析,以降低其结果的假阳性率.通过对Mascot与SEQUEST搜索结果进行比较,发现所得结果差异很大;利用多变量判别分析方法对Mascot及SEQUEST搜索结果进行判别分析,可有效提高SEQUEST结果中假阳性结果与正确结果之间的区分能力.对于Mascot搜索结果,采用多变量判别分析方法仍无法显著降低其假阳性结果,利用Decoy库搜索结果进行估计时亦存在导致错误估计的风险.     

啤酒主要成分的近红外光谱法测定

根据近红外光谱的振动吸收强度与有机分子官能团含量的线性关系,用偏最小二乘法,对啤酒的近红外光谱与其中的酒精度、原麦汁浓度以及总酸含量等3种主要成分进行了线性回归,并建立起相关的模型。用该模型对未知啤酒样品中的上述3种成分的含量进行预测,取得了令人非常满意的结果。可望作为啤酒厂的一种快捷而准确的检测方法予以推广。     

Diagnostics of calibration methods: model adequacy of UV‐based determinations

Models such as ordinary least squares, independent component analysis, principle component analysis, partial least squares, and artificial neural networks can be found in the calibration literature. Linear or nonlinear methods can be used to explain the structure of the same phenomenon. Each type of model has its own advantages with respect to the other. These methods are usually grouped taxonomically, but different models can sometimes be applied to the same data set. Taxonomically, ordinary least square and artificial neural network use completely different analytical procedures but are occasionally applied to the same data set. The aim of the study of methodological superiority is to compare the residuals of models because the model with the minimum error is preferred in real analyses. Calibration models, in general, are based on deterministic and stochastic parts; in other words, the data are equal to the model + the error. Explaining a model solely using statistics such as the coefficient of determination or its related significance values is sometimes inadequate. The errors of a model, also called its residuals, must have minimum variance compared to its alternatives. Additionally, the residuals must be unpredictable, uncorrelated, and symmetric. Under these conditions, the model can be considered adequate. In this study, calibration methods were applied to the raw materials, hydrochlorothiazide and amiloride hydrochloride, of a drug, as well as a sample of the drug tablet. The applied chemical procedure was fast, simple, and reproducible. The various linear and nonlinear calibration methods mentioned above were applied, and the adequacy of the calibration methods was compared according to their residuals.     

A REVIEW OF HYPHENATED CHROMATOGRAPHIC INSTRUMENTATION

Samples presented for chemical analysis are invariably mixtures, often very complex mixtures. This has led to the widespread acceptance and application of what have become called hyphenated chromatographic techniques. These techniques are combinations of chromatographic instrumentation with some (usually) spectroscopic technique. In this review, we treat the most important and useful of these combinations. The basic instrumental features of each method are described, and possible applications are discussed. The relative capabilities of each technique are weighed, and tradeoffs are discussed. In closing, a list of suggested further reading is provided.     

Rapid discrimination of slimming capsules based on illegal additives by electronic nose and flash gas chromatography

The discrimination of counterfeit and/or illegally manufactured medicines is an important task in the pharmaceutical industry for pharmaceutical safety. In this study, 22 slimming capsule samples with illegally added sibutramine and phenolphthalein were analyzed by electronic nose and flash gas chromatography. To reveal the difference among the different classes of samples, principal component analysis and linear discriminant analysis were employed to analyze the data acquired from electronic nose and flash gas chromatography, respectively. The samples without illegal additives can be discriminated from the ones with illegal additives by using electronic nose or flash gas chromatography data individually. To improve the performance of classification, a data fusion strategy was applied to integrate the data from electronic nose and flash gas chromatography data into a single model. The results show that the samples with phenolphthalein, sibutramine and both can be classified well by using fused data.     

Characterization of Biodiesel by Infrared Spectroscopy with Partial Least Square Discriminant Analysis

This paper reports a method to simultaneously classify 7% biodiesel and 93% diesel based on vegetable oil as the source of the biodiesel. Mafurra, moringa, and cotton biodiesel blends were characterized by infrared spectroscopy with partial least square discriminant analysis. The efficiency of model was evaluated based on the sensitivity and specificity. The model showed excellent results as all samples were correctly classified based on the raw material. Hence, the sensitivity and specificity parameters showed values of 1, which means 100% correct characterization of the samples in the calibration and prediction sets. Therefore, infrared spectroscopy with partial least square discriminant analysis is suitable for the characterization of biodiesel/diesel blends.     

Design and development of pyrolyser to extract medicinal oil from the stem of Ziziphus jujuba

Pyrolysis as an extraction method of phytochemicals from plant parts for medicinal applications is less explored. Practitioners of traditional Indian medicine use a process which is a crude equivalent of pyrolysis, to extract oily substances from stem parts of plants and use them in treatment of various ailments. In this study, a prototype pyrolyser is fabricated to simulate the traditional method and the stem part of Ziziphus jujuba is subjected to pyrolysis using the pyrolyser under controlled conditions. Based on the principle of applied pyrolysis, the engineering design is conceptualized and drawing for a prototype extractor is made. Material selection for the main reactor vessel and the heating system with controller is finalized. The prototype is fabricated. The oily extract obtained is compared with the extract from the traditional method for compositional identity and phyto chemistry to validate the process. The chemical similarities of the extracts from both methods establishes pyrolsis as the basic principle behind the traditional method and this validates the design of the pyrolyser. The FTIR and GC-MS analysis of the extracted oily substance from both methods reveals the presence of various cyclic, nitrogenous, long chain and heterocyclic compounds which are believed to be the pyrolysates of various cyclopeptide alkaloids reportedly present in the stem of Ziziphus jujuba. These phytochemicals have sedative property and are likely to be responsible for the curative nature of the oil used in the treatment of various human disorders and the research substantiates the stem's historical use by traditional practitioners.     

Multivariate quality control solved by one‐class partial least squares regression: identification of adulterated peanut oils by mid‐infrared spectroscopy

The Partial least squares class model (PLSCM) was recently proposed for multivariate quality control based on a partial least squares (PLS) regression procedure. This paper presents a case study of quality control of peanut oils based on mid‐infrared (MIR) spectroscopy and class models, focusing mainly on the following aspects: (i) to explain the meanings of PLSCM components and make comparisons between PLSCM and soft independent modeling of class analogy (SIMCA); (ii) to correct the estimation of the original PLSCM confidence interval by considering a nonzero intercept term for center estimation; (iii) to investigate the potential of MIR spectroscopy combined with class models for identifying peanut oils with low doping concentrations of other edible oils. It is demonstrated that PLSCM is actually different from the ordinary PLS procedure, but it estimates the class center and class dispersion in the framework of a latent variable projection model. While SIMCA projects the original variables onto a few dimensions explaining most of the data variances, PLSCM components consider simultaneously the explained variances and the compactness of samples belonging to the same class. The analysis results indicate PLSCM is an intuitive and easy‐to‐use tool to tackle one‐class problems and has comparable performance with SIMCA. The advantages of PLSCM might be attributed to the great success and well‐established foundations of PLS. For PLSCM, the optimization of model complexity and estimation of decision region can be performed as in multivariate calibration routines. Copyright © 2011 John Wiley & Sons, Ltd.     

主成分-人工神经网络在近红外光谱定量分析中的应用

近红外光谱的主成分由非线性迭代偏最小二乘法（ＮＩＰＡＬＳ）求出。主成分作标准化处理后,作为Ｂ－Ｐ神经网络的输入结点进行非线性迭代。该法的优点是,充分利用了全光谱的数据,得到消除噪声后的最佳主成分,能建立非线性模型,Ｂ－Ｐ神经网络迭代时间显著缩短。用该法对大麦中的淀粉含量进行了定量分析研究。结果为：校准和预测的相关系数分别为０．９８１和０．９５３,校准和预测的相对标准偏差分别为１．７０％和２．４８％。     

应用光谱技术无损检测油菜叶片中乙酰乳酸合成酶

应用可见/近红外光谱技术实现了油菜叶片中乙酰乳酸合成酶(ALS)的快速无损检测.对99个油菜样本进行光谱扫描,经过平滑、变量标准化、一阶求导等预处理后,应用偏最小二乘法(PLS)建立了ALS的预测模型.同时提取有效特征变量,作为反向传输人工神经网络(BPNN)和最小二乘-支持向量机(LS-SVM)的输入值,并建立相应的模型.用66个样本建模,33个样本验证.结果表明,LS-SVM模型能够获得最优的预测结果,预测集样本的相关系数(r)、预测标准差(RMSEP)和偏差(Bias)分别为0.998、 0.715和0.079,获得了满意的预测精度.结果表明,应用可见/近红外光谱技术结合LS-SVM检测油菜中乙酰乳酸合成酶是可行的,并能获得满意的预测精度,为进一步应用光谱技术进行油菜生长状况的大田监测奠定了基础.